Mean radiant temperature from global-scale numerical weather prediction models

• Published in: International journal of biometeorology Vol. 64; no. 7; pp. 1233 - 1245
• Main Authors: Di Napoli, Claudia, Hogan, Robin J., Pappenberger, Florian
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2020; Springer Nature B.V
• Subjects: Animal Physiology; bioclimatology; Biological and Medical Physics; Biometeorology; Biophysics; Computation; Earth and Environmental Science; Environment; Environmental Health; Environmental monitoring; Fluxes; Human biometeorology; humans; Meteorological satellites; Meteorology; Numerical weather forecasting; Original Paper; Plant Physiology; Prediction models; Radiation; Radiation data; Radiation measurement; Radiation monitoring; temperature; Thermal radiation; Weather forecasting; Validation; Mean radiant temperature; Numerical weather prediction; Radiation; Human comfort
• ISSN: 0020-7128; 1432-1254; 1432-1254
• DOI: 10.1007/s00484-020-01900-5

In human biometeorology, the estimation of mean radiant temperature (MRT) is generally considered challenging. This work presents a general framework to compute the MRT at the global scale for a human subject placed in an outdoor environment and irradiated by solar and thermal radiation both directly and diffusely. The proposed framework requires as input radiation fluxes computed by numerical weather prediction (NWP) models and generates as output gridded globe-wide maps of MRT. It also considers changes in the Sun’s position affecting radiation components when these are stored by NWP models as an accumulated-over-time quantity. The applicability of the framework was demonstrated using NWP reanalysis radiation data from the European Centre for Medium-Range Weather Forecasts. Mapped distributions of MRT were correspondingly computed at the global scale. Comparison against measurements from radiation monitoring stations showed a good agreement with NWP-based MRT (coefficient of determination greater than 0.88; average bias equal to 0.42 °C) suggesting its potential as a proxy for observations in application studies.